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This
program is associated
with Beisel et.al.
(2007), wherein we describe a likelihood ratio framework for analyzing
the fitness effects of beneficial mutations, focusing on
testing the null hypothesis that the distribution is exponential. We
also describe how to account
for missing the smallest effect mutations, which are often difficult to
identify
experimentally. We also describe how to pool data across
experiments, since we expect few possible
beneficial mutations in any particular experiment.
INSTRUCTIONS
- The programs are
written in R. R is a language
and environment for
statistical computing and graphics. Download R and
install. Make sure that you familiarize yourself with the use of
R before using the following programs.
- Download,
read and follow the instructions in the guide,
before
running these two programs. The guide will provides some useful
information about testing the domain of attraction for adaptive mutants.
PROGRAM
evalrtfunc.R
These are the supporting functions for evalrt.R and evalrtmc.R
which implement methods described in Beisel et. al., "Testing the
extreme value domain of attraction fordistributions of beneficial
fitness effects." Genetics 2007.
evalrt.R
This program implements the LRT for the Gumbel domain of attraction
against a GPD alternative as implemented in Beisel et. al.,
"Testing the extreme value domain of attraction for distributions
of beneficial fitness effects." Genetics 2007.
evalrtmc.R
This program implements the LRT for the Gumbel domain of attraction
against a GPD alternative incooperating measurement error as
implemented in Beisel et. al., "Testing the extreme value domain of
attraction for distributions of beneficial fitness effects."
Genetics 2007.
EXAMPLE DATA
You may
download a
text file with a sample data set by clicking
(here)
. You can replace this file with your own data set.
If you have any questions
on running these programs contact
Craig Beisel [craig@beisel.net]
or
Darin Rokyta [rokyta@uidaho.edu]
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